Asynchronous context communication for speech services

ABSTRACT

A remote personal assistant server/service is configured with a plurality of different state machines for supporting asynchronous communications with the front-end interfaces of personal assistants and for supporting the interfacing with a plurality of proprietary back-end intelligence bots, which are independently controlled and separate from the remote personal assistant service/server.

BACKGROUND Background and Relevant Art

Computers and computing systems have affected nearly every aspect ofmodern living. Computers are generally involved in work, recreation,healthcare, transportation, entertainment, household management, etc.

Computing system functionality can be enhanced by a computing systems'ability to be interconnected to other computing systems via networkconnections. Network connections may include, but are not limited to,connections via wired or wireless Ethernet, cellular connections, oreven computer to computer connections through serial, parallel, USB, orother connections. The connections allow a computing system to accessservices at other computing systems and to quickly and efficientlyreceive application data from other computing systems.

Interconnection of computing systems has facilitated distributedcomputing systems, such as so-called “cloud” computing systems. In thisdescription, “cloud computing” may be systems or resources for enablingubiquitous, convenient, on-demand network access to a shared pool ofconfigurable computing resources (e.g., networks, servers, storage,applications, services, etc.) that can be provisioned and released withreduced management effort or service provider interaction. A cloud modelcan be composed of various characteristics (e.g., on-demandself-service, broad network access, resource pooling, rapid elasticity,measured service, etc.), service models (e.g., Software as a Service(“SaaS”), Platform as a Service (“PaaS”), Infrastructure as a Service(“IaaS”), and deployment models (e.g., private cloud, community cloud,public cloud, hybrid cloud, etc.).

Cloud and remote based service applications are prevalent. Suchapplications are hosted on public and private remote systems such asclouds and usually offer a set of web-based services for communicatingback and forth with clients.

Many computers are intended to be used by direct user interaction withthe computer. As such, computers have input hardware and software userinterfaces to facilitate user interaction. For example, a moderngeneral-purpose computer may include a keyboard, mouse, touchpad,camera, etc. for allowing a user to input data into the computer. Inaddition, various software user interfaces may be available.

Examples of software user interfaces include graphical user interfaces,text command line-based user interface, function key or hot key userinterfaces, speech to text interfaces, gesture interfaces, sensorinterfaces and the like.

A relatively new and powerful development in computer technology is theconfiguration of a personal digital assistant, also referred to hereinas simply a personal assistant. Examples of personal assistants includeMicrosoft's Cortana™, Apple's Siri™ Amazon's Alexa™, Samsung's Bixby™and Google's Google Assistant.

Personal assistants comprise powerful computing system infrastructuresand interfaces that include speech to text interfaces for enabling auser to speak to the personal assistant to perform a query and/or toinitiate another function with the personal assistant. The personalassistant is configured with a front-end interface, such as the speechto text interface, for receiving user input and for providing outputfrom the personal assistant, as well as back-end intelligence forperforming the queries and functions requested by the user. Typically,the personal assistant stores/maintains personal information about theuser and user devices to customize and render the desired functions andoutput, on demand, and in a personalized manner.

While personal assistants can be very personal and useful, they aresomewhat limited in their applicability to new and diverse computingenvironments, such as the automobile industry and other enterpriseenvironments that may invoke proprietary interfaces and applications.For instance, if a developer wants to create new applications orinterfaces for facilitating personal assistant functionality within anew computing environment (e.g., an automobile), the developer istypically constrained to create the new applications/interfaces asplug-ins or shim layer applications that interface with the one or morepre-existing personal assistant(s). They are not permitted, for example,to control the back-end intelligence modules that are integrated intothe existing personal assistant.

Alternatively, the developer must create an entirely new and proprietarystand-alone personal assistant, with built-in speech/interface servicesand back-end intelligence, which can be prohibitively expensive.

Currently, there is no mechanism for enabling developers to createcustomized and proprietary personal assistants, which includeproprietary back-end intelligence/functionality, particularly in amanner that enables the personal assistants to still leverage commonspeech services utilized by other personal assistant and which furtherenable the leveraging of and interfacing with other personal assistants.

Another problem with existing personal assistants is that they requiresynchronous processing of commands for the devices/applications thatthey interface with through the personal assistant, including theutilizations of developer plug-in/shim layer applications. This is aresult, at least in part, due to conventional personal assistantsutilizing only a single state machine for processing the user input anddata passed between the front end of the personal assistant (such as thespeech to text interface) and the assistant's back-end intelligence.

For instance, if a user is talking to a personal assistant to perform aquery, the processing of the speech by the personal assistant must becompleted prior to the audible response data being spoken back to theuser by the personal assistant. It is also not possible for the user toinitiate or receive other processing through the personal assistant,until each prior process that was initiated with the personal assistantis completed, synchronously, one at a time. In this regard, users arenot able to benefit from multi-task functionality with existingconfigurations of personal assistants.

Yet another problem with existing personal assistants is that they areprovided significant exposure to a user's personal information, as wellas significant control over the way in which the back-end intelligencedecides to use and share this personal information. This exposure andcontrol to a user's personal information can help the personal assistantprovide personalized and targeted advertisements and suggestions thatmay be appreciated and helpful to a user. However, this substantialexposure and control to a user's personal information can also,sometimes, create privacy concerns for a user. Such a concern, forinstance, is the potential for the user's personal information to beshared by the personal assistant with other services and applicationslinked to by the personal assistant, without the user's knowledge andpossibly against the user's wishes.

The configuration of existing personal assistants to provide broadfunctionality to support a very broad range of the differentapplications and utilities, can also cause the back-end intelligence ofthe personal assistant to be somewhat unwieldy and to incur undesireddelays, particularly when compared to more specialized services/systemsthat do not support such a broad range of applications.

While the subject matter claimed herein is not limited to embodimentsthat solve any disadvantages or that operate only in environments suchas those described above, it will be appreciated that there is anongoing need to provide systems and methods for improving the way inwhich personal assistants are configured and utilized.

BRIEF SUMMARY

Disclosed embodiments include systems, methods and devices configured tofacilitate and support customized personal assistant services and, insome instances, to provide and/or support asynchronous speech servicesthat can be used with personal assistants.

One embodiment illustrated herein includes a remote personal assistantservice or server that has a plurality of different state machines forsupporting asynchronous communications with the front-end interfaces ofpersonal assistants and for supporting the interfacing with a pluralityof proprietary back-end intelligence bots, which are independentlycontrolled and separate from the remote personal assistantservice/server.

In some instances, the remote personal assistant server facilitates andsupports customized personal assistant services, including at leastasynchronous speech services for one or more remotely connected personalassistants.

In some instances, the personal assistant server comprises a pluralityof state machines, including a first input state machine which managesstate and processing flow of a first type of input which is receivedfrom a speech interface for one or more remotely connected personalassistants, including at least a first personal assistant, as well as asecond input state machine which manages state and processing flow of asecond type of input which is received from one or more interfaces otherthan the speech interface of the one or more remotely connected personalassistants. The plurality of state machines also includes at least oneoutput state machine which manages state and processing flow of outputprovided to the one or more remotely connected personal assistants inresponse to at least the first and second type of input.

The remote personal assistant also includes one or more processors andone or more computer-readable media having stored instructions that areexecutable by the one or more processors to cause the personal assistantserver to implement one or more methods for supporting asynchronousspeech services, as disclosed herein.

Disclosed methods implemented by the remote personal assistant include aplurality of acts performed by the remote personal assistant. These actsinclude the act of instantiating, exposing or otherwise providing aplurality of different state machines, such as the first and secondinput state machines and the at least one output state machinereferenced above. The acts also include the remote personal assistantreceiving first input from a first personal assistant, which includesthe first type of input (e.g., speech input), wherein the first inputstate machine controls the state and processing flow of the first inputat the personal assistant server.

Then, the first input is transformed, such as by being converted into adifferent form and/or augmented with different information, such ascontextual information based on other input data received by the remotepersonal assistant.

Then, once output corresponding to the first input is received, it issent to the first personal assistant asynchronously from the processingflow of the first input controlled by the first state machine.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

Additional features and advantages will be set forth in the descriptionwhich follows, and in part will be obvious from the description, or maybe learned by the practice of the teachings herein. Features andadvantages of the invention may be realized and obtained by means of theinstruments and combinations particularly pointed out in the appendedclaims. Features of the present invention will become more fullyapparent from the following description and appended claims, or may belearned by the practice of the invention as set forth hereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features can be obtained, a more particular descriptionof the subject matter briefly described above will be rendered byreference to specific embodiments which are illustrated in the appendeddrawings. Understanding that these drawings depict only typicalembodiments and are not therefore to be considered to be limiting inscope, embodiments will be described and explained with additionalspecificity and detail through the use of the accompanying drawings inwhich:

FIG. 1 illustrates an embodiment of a conventional personal assistantsystem architecture in which a personal assistant is configured with asingle state machine and back-end intelligence.

FIG. 2A illustrates an embodiment of a personal assistant systemarchitecture in which a remote personal assistant server/service isconfigured with a plurality of state machines and is interposed betweenthe front-end personal assistant interface and one or more remoteback-end intelligence bots.

FIG. 2B illustrates an embodiment of a personal assistant systemarchitecture, like the architecture of FIG. 2A, but wherein the remotepersonal assistant server/service is shown as interfacing with aplurality of different personal assistants and in which securityfeatures are implemented to control and add privacy to communicationstransmitted between the personal assistant front-ends and the back-endintelligence bots through the remote personal assistant server/service.

FIG. 3 illustrates a processing flow diagram that reflects the flows oftwo different processes that are performed asynchronously, byimplementing aspects of the systems/methods disclosed herein.

FIG. 4 illustrates a processing flow diagram that reflects a pluralityof different processes that are performed asynchronously, byimplementing aspects of the systems/methods disclosed herein.

FIG. 5 illustrates another processing flow diagram that reflects aplurality of different processes that are performed asynchronously, byimplementing aspects of the systems/methods disclosed herein.

FIG. 6 illustrates a flow diagram with a plurality of acts associatedwith methods, disclosed herein, from the perspective of a remotepersonal assistant service/server, for facilitating and supportingcustomized personal assistant services and, in some instances, toprovide and/or support asynchronous speech services that can be usedwith personal assistants.

DETAILED DESCRIPTION

As previously noted, the disclosed embodiments generally includesystems, methods and devices configured to facilitate and supportcustomized personal assistant services and, in some instances, toprovide and/or support asynchronous speech services that can be usedwith personal assistants.

In some embodiments, a remote personal assistant service or server isconfigured with a plurality of different state machines for supportingasynchronous communications with the front-end interfaces of personalassistants and for supporting the interfacing with a plurality ofproprietary back-end intelligence bots, which are independentlycontrolled and separate from the remote personal assistantservice/server.

The disclosed configurations for the remote personal assistant servercan help facilitate the manner in which an enterprise or other entitycan deploy and use a customized/proprietary personal assistant with oneor more front-end interface(s) for interfacing with a user, and whichleverages some common tools (such as speech services) by an intermediarypersonal assistant server having a plurality of state machines tosupport asynchronous communications with the personal assistant, andwhich also utilizes functionality of customized and proprietary personalassistant back-end intelligence bots that are separate from the personalassistant server, and which can be customized by the developer of thepersonal assistant to provide output that is desired/rendered with thepersonal assistant's front-end interface(s).

As will be described in more detail herein, this configuration ofsplitting a personal assistant front-end from the back-end intelligence,with an intermediary personal assistant server having a plurality ofdifferent state machines for facilitating asynchronous communicationswith the personal assistant(s), can provide many technical benefits andpractical applications, including addressing many of the problemsidentified in the background section of this application.

These benefits and applications may include, for instance, facilitatingthe way a personal assistant can be used to perform multipleasynchronous tasks with a user, including asynchronous speech services,and for supporting multiple different personal assistants that sharefunctionality of a common intermediary personal assistant server. Thebenefits that can be realized also include, in some instances, areduction in delays that could otherwise be experienced by conventionalpersonal assistants that are more monolithic and centrally controlled.The benefits of the disclosed embodiments can also include a decreasedrisk of exposing and losing control to confidential personalinformation, as compared to risks associated with providing personalinformation to a conventional personal assistant.

Attention will now be directed to the figures to illustrate thefunctionality and features of the disclosed personal assistantembodiments and some distinctions and benefits over conventionalpersonal assistant systems.

FIG. 1, for instance, illustrates a computing architecture 100 of aconventional personal assistant system. As shown, the personal assistantserver 125 is configured with a single state machine 130 and back-endintelligence 135 for processing client requests that are generated by auser 105 at a device of a client 110. As also shown, the client 110includes a plurality of applications (e.g., App 1, App 2, App 3) and afront-end personal assistant UI for enabling the client to interfacewith the personal assistant server 125 processes the logic forsatisfying the client requests at the back-end intelligence 135, whichmay also access stored data (such as a user's personal information andother information stored in storage 145) and remote cloud resources 150to obtain additional information needed to satisfy the requests.

As previously noted, the centralized control over a user's personalinformation at the personal assistant server may be undesirable in someinstances, particularly if there are concerns about how that informationwill be used and accessed by other systems. Additionally, thecentralized control of back-end intelligence can help the personalassistant server 125 support many different types of applications andfunctionalities, it can also become somewhat unwieldy and cause delaysfor processing certain application requests that could be processed morequickly, in some instances, by more specialized systems.

FIG. 2A illustrates an embodiment of a personal assistant systemarchitecture 200A in which a remote personal assistant server 250 orservice is configured with a plurality of state machines, including atleast two different input state machines (e.g., 202 and 204) and atleast one output state machine (206) to facilitate asynchronouscommunications and interfacing with the personal assistant 220 of aclient 210. The personal assistant 220 of the client 210 also includes aplurality of state machines, including at least two different inputstate machines (222, 224) and at least one output state machine 206 tofacilitate the asynchronous communications.

The state machines manage the state and the processing flow of theinputs and outputs passing through the personal assistant 220 and remotepersonal assistant server 250. For instance, input state machine 222 isa dedicated state machine for managing state and processing flow of afirst type of input (e.g., speech/audio inputs) which is received fromthe microphone or other hardware 245 of the client device and that isprocessed by the speech interface 232 in route to the remote personalassistant server 250.

Likewise, a second input state machine 224 is a dedicated state machinefor managing state and processing flow of a second type of input (e.g.,data other than speech, such as sensor data) which is received from oneor more hardware components 245 (which can include any type of hardwaresensor) and which is processed by corresponding interfaces (e.g., 230)in route to the remote personal assistant server 250.

While only two input state machines are shown, there may also be others,dedicated to processing inputs for one or more predetermined types ofdata. In some instances, however, the management of state and processingflow for all data inputs are managed by the second input state machine224, other than for speech input, which is managed by the first inputstate machine 222.

The output state machine 206 is configured to manage state andprocessing flow of all output received from the remote personalassistant server 250, as managed by the output state machine 206 of theremote personal assistant server 250.

The remote personal assistant server 250 also includes a plurality ofstate machines, including at least a first input state machine 202,which is a dedicated state machine for managing state and processingflow of a first type of input (e.g., speech/audio inputs), which isreceived from the speech interface 232, which is further processed andtransformed by a speech service/application 234 at the remote personalassistant server 250, and/or further transformed by being augmented withcontextual information from other interfaces 240 at the remote personalassistant server 250, prior to be provided to the back-end intelligencebots 280.

Likewise, a second input state machine 204 is a dedicated state machinefor managing state and processing flow of a second type of input (e.g.,data other than speech, such as sensor data) which is received from theother interfaces (e.g., 230) of the personal assistant and which isprocessed and used as and/or to obtain contextual information forsupplementing information provided to the back-end intelligence bots280.

Multiplexers and de-multiplexers (Mux/DeMux) at the personal assistant220 and remote personal assistant server 250 serialize/multiplex anddeserialize/demultiplex the outgoing and incoming packets of data,respectively, from the different interfaces, according to the processingflows that are managed by and controlled by the different state machinesand enable processing/transmission of different packets according totheir specified protocols.

According to some embodiments, the protocols for transmitting thepackets, is a standard Transmission Control Protocol (TCP) protocol or aUser Datagram Protocol (UDP). Other protocols, such as USP and otherprotocols can also be used. In some instances, a proprietary protocolthat is known to a particular personal assistant and back-endintelligence bot, but which is not known to other personal assistantsand back-end intelligence bots is used, or a standard protocol that usesproprietary encryption known only by the particular personal assistantand corresponding intelligence bot, but not other personal assistants orback-end intelligence bots, is used to transmit the packets of databetween the systems.

The states of the different input/output packets are maintained andupdated by the state machines in one or more data structures (e.g.,tables) within corresponding storage of the systems. The states of thedifferent packets and communications, as maintained, will enable orprevent different packets from being transmitted according to thedifferent protocols and communication policies established by and/orused by the interfaces to communicate between the different systemcomponents.

It will be appreciated that the use of a plurality of separate anddedicated state machines, with the multiplexers/demultiplexers (e.g.,Mux's/DeMux's) enables asynchronous communications/transmissions ofpackets for data of different types. For instance, this enablesasynchronous transmission of speech data along with other data of adifferent type, such as sensor data that can provide context for thespeech data, to be processed and transmitted concurrently/asynchronouslywith the speech data, and which are all multiplexed together in one ormore streams of data packets transmitted between the system components.These streams of data can also be demultiplexed and processed by thedifferent system components/interfaces according to the managed stateand processing flow controlled by the different state machines.

The manner in which the state machines and multiplexers/de-multiplexersfacilitate the asynchronous communications will be more apparent in thedescription of FIGS. 3-5, further below.

As also shown in FIG. 2A, the personal assistant 220 also includes afront-end interface 230, which may include user interfaces andcommunication interfaces for facilitating communications between theuser 205 and the client 210 applications with the personal assistant 200and to additionally facilitate communications between the personalassistant 220 and the remote personal assistant server 250, viacorresponding interface(s) 240 at the remote personal assistant server250. These interfaces may include APIs (application programminginterfaces), as well as graphical and audio interfaces.

The personal assistant 220 also includes a speech service/application232 which is configured to receive and process audio received at theclient, via one or more hardware components 245 (e.g., a microphone) andto transmit that audio as speech input to the remote personal assistantserver 250. It can also receive speech inputs (comprising output data)from the remote personal assistant and to provide that speech as audiothat is rendered by the client as audio via one or more hardwarecomponents 245 (e.g., a speaker).

The remote personal assistant server 250 also includes a correspondingspeech service/application 234 that is configured to receive the speechinput and to convert it into text, which can be provided to one or moreback-end intelligence bots 280 (e.g., 282, 284, 286), as well as toconvert outputs from the back-end intelligence bots 280 into speech datathat is provided as speech input for the personal assistant 220 torender to the user 205.

The back-end intelligence bots 280 are configured as specialized andproprietary back-end intelligence bots for the personal assistant 220 toperform specialized functions with the data they receive and to provideoutput that can be provided back to the personal assistant 220 at theclient.

In some instances, each back-end intelligence bot is developed with thepersonal assistant it corresponds with, so as to enable it to performspecialized functions associated with the personal assistant. In thismanner, the personal assistant 220 and back-end intelligence bot(s)(e.g., 282, 284 and/or 286) corresponding to it can perform functionsmore quickly and nimbly than conventional personal assistant systemsthat are configured to address a broader range of functionality (such asfunctionality enabled by different personal assistants/bots that areproprietary to a different enterprise/entity).

In some instances, the corresponding back-end intelligence bot is uniqueto only that personal assistant and is not shared with any of the otherpersonal assistants that are connected to the remote personal assistantserver 250. In this manner, it is possible to maintain greater controlover proprietary and/or confidential information used by the personalassistant and back-end intelligence bot.

In one example, an enterprise, such as a car manufacturer (e.g., BMW),can create their own proprietary personal assistant 220 andcorresponding back-end intelligence bot(s) (e.g., 282 and or anotherbot) to perform a limited set of functions that are specialized andunique to the BMW cars (e.g., navigation to BMW dealerships ormechanics, maintenance routines, etc.), such that a user 205 (e.g.,driver) could invoke the personal assistant and services of thatassistant while driving, by speaking ‘BMW, please tell me when my oilchange is due.’

In this example, the BMW personal assistant could convert that audio tospeech input that is sent to the remote personal assistant server 250and converted into text that is sent to the back-end intelligence bot(e.g., 282) for BMW that evaluates requirements for that user's car andthe last scheduled maintenance and can provide the correct output answerthat is sent back through the remote personal assistant server(converted back to speech data) that is then rendered by the BMWpersonal assistant as audio for the user, telling the user when the nextoil change is due.

To further assist a user, the client device (which may comprise a car inthis instance), may also include sensors within the shown clienthardware 245 (e.g., oil level sensors, air tire sensors, GPS sensors,temperature sensors, cameras, calendar data and/or any other inputcomponent or sensor) to provide additional sensory data to the remotepersonal assistant that is augmented by the remote personal assistantserver 250 to the data it provides to BMW's back-end intelligence bot.In such an example, the BMW's back-end intelligence bot may proactivelyprovide instructions/output that indicates that an oil change or otherfluid refill is suggested or due, based on a detected condition orcontext determined from the information that is obtained from the othersensors/sensor data.

For instance, the instruction may indicate that the tire(s) need to befilled with additional air pressure, due to detected temperature, roadconditions, detected pressure levels in the tires, etc. The informationprovided to the user may also specify a location and navigation promptsfor finding BMW mechanic or dealership where to obtain new tires or havemaintenance performed, based on the detected contextual information.

Notably, and importantly, this information can also be relayed andprovided to the user, proactively, and asynchronously, while the user isengaging in speech dialog with the personal assistant for another issue(e.g., the user has initiated a query and is awaiting a response and/orthe user is in the middle of dictating a query, and/or while the user isutilizing the personal assistant to perform functionality with adifferent application at the client).

In such instances, the asynchronous and proactive informationcommunication can be provided to the user via the personal assistantwith one or more interfaces (e.g., visual and/or audio), even if itmeans that the user's current dialog is interrupted or simultaneouslyoccurring, and even though the user may not have been inquiring for theinformation provided (e.g., they may have been inquiring about an oilchange and received information about tire maintenance and locations forgetting service), before, during and/or after the information theyinquired about.

It will be appreciated that the functionality of each back-endintelligence bot can be customized for each enterprise and developer toaccommodate different needs and preferences for each correspondinglyunique/proprietary personal assistant. This functionality also includesprioritization and filtering of data to determine what outputs should beprovided to a user, based on the contextual information that is received(e.g., sensor data) in combination with the speech data that isreceived. In some instances, different terms, tuples, phrases or otherinputs comprise key terms or profiles that map to proprietary outputsthat are programmed into the different bots by the developers. In someinstances, the mappings, are stored in storage at the remote personalassistant server. In other instances, this information is proprietaryand confidentially maintained at the back-end intelligence bots withoutbeing provided to the remote personal assistant server 250.

In some instances, there is only a single back-end intelligence bot(282, 284 or 286) for each personal assistant 220. In other instances,there are multiple back-end intelligence bots associated with and linkedto by the different personal assistant. The remote personal assistantserver 250 maintains a mapping of associations between the differentpersonal assistants and back-end intelligence bots to ensure client datais maintained separately and not provide to the wrong entities. Thesemappings are maintained in the storage. In some instances, the statemachines can also be used to help control which packets are transmittedto the different personal assistants and back-end bots based onidentifiers that can be attached to the data packets to identify sourceand destination of the packets.

It is noted that the different systems in the architecture 200A, such asthe client 210, personal assistant 220, remote personal assistant server250 and back-end intelligence bots 280 may each include and/or use oneor more processor(s) (those shown and/or other processors that are notshown), which include hardware processors that execute executableinstructions to enable interactions between the differentillustrated/described components and to facilitate the functionalitydescribed herein.

It will also be appreciated that the different systems (e.g., client210, personal assistant 220, remote personal assistant server 250 andback-end intelligence bots 280) as well as each of the differentcomponents may be connected through one or more wired and/or wirelessconnections. Additionally, while some of the systems are shown asdiscrete systems that are circumscribed into an illustrated box, it willbe appreciated that each of these systems may be a stand-alone system ora distributed system that includes two or more remotely connectedcomputer systems/components. Additional descriptions of the computingenvironment and types of computing systems that may be used with orincorporated into the described embodiments are provided at the end ofthis disclosure.

Attention will now be directed to FIG. 2B, which illustrates anembodiment of a personal assistant system architecture 200B, similar tothe architecture of FIG. 2A, but wherein the remote personal assistantserver/service is shown as interfacing with a plurality of differentpersonal assistants and in which security features are implemented tocontrol and add privacy to communications transmitted between thepersonal assistant front-ends and the back-end intelligence bots throughthe remote personal assistant server/service.

It will be noted that the different personal assistants that can beconnected with the remote personal assistant server 250, along with theaforementioned personal assistant 220, can include any combination ofsimilar, identical, different, proprietary, unique personal assistants,each of which may be connected, via the remote personal assistant server250, to any combination of one or more similar, identical, different,proprietary or unique back-end intelligence bots 280 than the one ormore bot(s) used by other personal assistant(s).

In some instances, a same client (e.g., client 210) utilize a pluralityof different personal assistants (e.g., personal assistant 220 and 221,each of which is configured with similar components as previously shownand described with reference to FIG. 2A, but which is connected todifferent back-end intelligence bots 280 and or that interface withdifferent client applications, for enabling different/uniquefunctionality and user experiences). In yet other instances, differentclients (e.g., client 210 and client 212) have different proprietarypersonal assistants and may have different applications and link todifferent back-end bots to provide entirely different functionality anduser experiences. For instance, by way of example, client 212 may be aschool learning device and the personal assistant 222 may be designed tointerface with a back-end intelligence bot (e.g., 286) for performing aschool exam, or another type of client and bot combination for providingyet different functionality which is entirely different than the clientdevice and functionality provided by client 210 and personal assistant220.

In other instances, not shown, different clients (e.g., client 210 andanother client on a different device) share a common or similar personalassistant (e.g., personal assistant 220), to provide similarfunctionality to a classified type of user, such as all employees of anenterprise that all utilize a same or similar type of client devicehaving similar applications.

In some instances, the plurality of state machines (e.g., at least twoinput state machines and at least one output state machine) provided bythe personal assistant are shared between different personal assistantson a same client, or when provided as a service, for the same personalassistant service. In other instances, a separate set of the pluralityof state machines is instantiated/provided for each instance of thepersonal assistant that is used.

The remote personal assistant server 250 may also provide/instantiateprovide a separate set of the plurality of state machines shown, foreach personal assistant it interfaces with and/or each personalassistant session with a client that it participates. Alternatively, oradditionally, it may share a set of the state machines with differentclients, personal assistants, personal assistant instances and/orpersonal assistant communication sessions.

The foregoing examples, are merely illustrative of the flexible scopeand utility of the configuration described above, for separating theback-end intelligence bots away from the remote personal assistantserver, to facilitate the deployment and use of customized andproprietary personal assistants and for using a plurality ofdifferent/dedicated state machines for facilitating the asynchronouspersonal assistant services, including speech services with contextualinformation.

FIG. 3 illustrates a processing flow diagram 300 that reflects the flowsof two different processes that are performed asynchronously, byimplementing aspects of the systems/methods disclosed herein.

As shown, data is received and processed by one or more of theprocessing systems that have described above (e.g., the personalassistant, personal assistant server, and intelligence bot(s)). Theseprocessing systems are collectively illustrated as block 350, forconvenience.

As also shown, these systems 350 enable asynchronous communications tooccur, such that speech data can be received and can start to beprocessed while other data is also received, such as UI input data(e.g., input entered at a touch screen or other device or data detectedby a sensor). This additional data 310 can be received and can beprocessed asynchronously with the speech data, without causing an error,because of the use of different state machines, including at least onededicated to the speech data 305. Then, the response to the speech datacan be provided with the UI output data, asynchronously.

In one non-limiting example of the foregoing processing flow, a user cantell their personal assistant to enlarge a selected item on a map. Theuser can simultaneously or subsequently select the item/area on the mapshowing on a touch screen. The personal assistant systems will processthis data (with the instruction being processed/transformed at thepersonal assistant server into an instruction that is translated andinterpretable at the appropriate intelligence bot, augmenting theinstruction with context of the portion of the map that has beenselected) and which causes the back end bot to identify whether theinstruction is appropriate and, if so, to identify a map database toobtain an enlarged map of the selected item/area, and to provide this asoutput that is processed and passed back to the user as the enlarged map(e.g., UI output data 325) and a confirmation as response data 320confirming the action (e.g., speech that rendered to the user, such as‘here you go’). Notably, such functionality is not previously possiblewith conventional personal assistants that operate with a single statemachine and that are not capable of processing the other input data 310asynchronously, and which must wait to receive/process the other data310 until after the managed state for the first speech session isreflected as processed and completed.

FIG. 4 illustrates another processing flow diagram that reflects aplurality of different processes that are performed asynchronously, byimplementing aspects of the systems/methods disclosed herein, and whichcan facilitate asynchronous speech services based on or with contextualinformation.

In this flow 400, the system 450 processes inputs and outputs, such assensor data 405 and 415, which may be received from any number ofsensors and hardware components (e.g., GPS devices, gyroscopes, pressuregauges, cameras, microphones, timers, counters, weather and temperaturesensors, and other sensors). Accordingly, this sensor data, for example,may include GPS data, temperature data, pressure data, velocity data,light data, camera data, timing data, and other data.

As shown, the speech data 410 is reflected as a partially filled box.This is to indicate a scenario in which a user starts to speak a command(e.g., ‘Assistant, please tell me . . . . ’), but does not yet finishthe command before it starts to be processed by the system in such amanner as to generate a response. In this instance, for example, theprocessing can include transforming the speech to text and augmentingthe data with historical or contextually relevant data accessible to thesystem, such as a current event data, a calendar event data, ahistorical pattern, positioning data, or any other contextualinformation. The system components (as previously described) can thengenerate a response based on the input that has been processed up tothat point, along with the contextual information that is has received(e.g., data 405 that may include current positioning information and/ortime information and/or weather/temperature information).

In this example, the system may preempt the user's remaining speech byproviding a response that is an inferred response 425, such as ‘thetemperature is 70 degrees’, or ‘the outcome of the vote is 55 to 45 infavor of the bill’, or ‘turn left at the next street’, or ‘you have 5more miles to go’, etc. These inferred responses may be based onprocessing any combination of the data 405, the speech data 410 and/orother contextual information the system is able to access with thepersonal assistant server and/or corresponding intelligence bot(s), aspreviously described.

Importantly, it is noted that the inferred response is receivedasynchronously from the speech input processing, and this is enabled bythe configurations of the system 450, as previously discussed, withseparate state machines to manage the flow of the speech inputseparately from the state of the output and other inputs.

The flow 400 also shows how the system may provide a different or fullresponse 430, based on additional information (e.g., data 415) which isprocessed and used to generate the full/different response 430, butwhich may not have been considered prior to the generation of theinferred response 425. For instance, if the temperature changes, asdetected by data 415, the system may send a corrected/updated temperateas the full/different response 430. In this instance, the inferredresponse 425 was queued up for sending to the user independently ofknowing that the new data 415 had been received. Accordingly, becausethe states are managed separately, with different state machines, thisnew data could be received and could start to be processed concurrentlywith the inferred response being finalized and transmitted to the user.Likewise, the sending of the inferred response could now be used ascontextual information for the system to know that a new full/differentresponse 430 should be generated and sent to the user in response to theadditional data 415 that is received. Again, this can be performedasynchronously.

FIG. 5 illustrates another processing flow diagram that reflects aplurality of different processes that are performed asynchronously, asenabled by implementing aspects of the systems/methods disclosed herein.

As shown, an application or device is started, which is connected to thepersonal assistant, as reflected by the start App/Device input 505. Thistriggers a process of instantiating one or moreapplications/drivers/interfaces. This processing flow for launching theapplication/device is managed through a first set of one or more statemachines of the system 550 and which may include accessing/relying onoutput provided by the intelligence bots and which may be further basedon contextual information provided to the bots by the personal assistantserver when it processes and transforms (e.g., translates, augments) theinput/information provided to the intelligence bots. In some instances,the App/Device input 505 is simply a request to start an application orto enable/connect a device. Based on the output from the intelligencebot(s) associated with the personal assistant, a corresponding responseor instruction will be received. This response may include App/DeviceData 515.

Notably, and asynchronously, prior to the App/Device data 515 beingreceived (which may be, for example, an instruction for enabling theapplication/device to launch and/or a confirmation that it has launchedand that may be rendered as audio or visual data), the system 550receives a stream of speech data 530. In one instance, the startApp/Device input 505 is entered as touch input at a touch screen deviceof the client device by a user and which is sufficient to trigger thelaunching of an email application and interface display at the touchscreen. In this example, the speech data is a request spoken by the userand detected by a microphone of the client device, for the personalassistant system to send an email to a target entity.

Then, while the speech data 530 is being received, the application islaunched and this results in the system confirming that the email hasbeen sent (e.g., response to speech 525). The processing of sending theemail may include transforming the speech to a translated instructionfor the intelligence bot and then obtaining output from the bot that theemail was sent. The email may be sent using contact information and/orcommunication protocols available to and known by the personal assistantand/or corresponding intelligence bot, which are proprietary and notknown to the personal assistant server or other personal assistants. Forinstance, the personal assistant may encrypt contact informationobtained from an application/storage on the client's device using a key(e.g., 283A) and that information can be transmitted securely to theintelligence bot 282 for decryption using a corresponding key 283B priorto performing its other functions.

The flow 500 of FIG. 5 illustrates how additional information (e.g., astart App/Device request) can be received and processed, as anindependent request, asynchronously, from speech data 530 that isreceived and processed and how this provides enhanced functionality andutility for personal assistants that was not possible with conventionalsystems.

In some instances, some additional information 520 can include aproactive and unrequested notification by the system which may berelated to the speech input 530 and which is triggered by the systemwhen it determines (based on the speech 530, start app/device 505 input,other sensor input and/or other contextual information) that thenotification should be provided. In one instance, for example, the otherdata 520 comprises a notification that the recipient has acknowledgedreceipt of the message or forwarded, replied to, or deleted the message.In other instances, the additional information 520 can also becompletely unrelated to the speech data 530 that is being received, suchas a notification of what time it is or what the temperature is or howfast the user is moving, etc.

Importantly to note, however, is that the system does not have to waitfor the stream of speech data 530 to be processed and in a completedstate before the other data 520 is provided through the same personalassistant that is processing the speech input. This is because of theuse of different state machines by the same personal assistant and theother configurations described herein. Instead, the other data 520 canbe received asynchronously from the speech data 530, even interrupting(speaking to the user as rendered through a speaker and as managed asoutput data with the output state machines), while the user issimultaneously speaking (as detected by the microphone and as managed asinput data with the input state machines).

The following discussion for FIG. 6 now refers to a number of methodsand method acts that may be performed by a personal assistant system orserver, such as described in reference to FIGS. 2A and 2B. Although themethod acts may be discussed in a certain order or illustrated in a flowchart as occurring in a particular order, no particular ordering isrequired unless specifically stated, or required because an act isdependent on another act being completed prior to the act beingperformed.

Further, the methods may be practiced by a computer system including oneor more processors and computer-readable media such as computer memorywhich store computer-executable instructions that when executed by oneor more processors enable or cause various functions to be performed,such as the acts recited in the disclosed embodiments.

The flowchart 600 in FIG. 6 includes a plurality of acts that areimplemented by a personal assistant system or server for facilitatingand supporting customized personal assistant services and to at leastsupport asynchronous speech services for one or more remotely connectedpersonal assistants.

These acts, include an act of providing a plurality of different statemachines (act 610). As noted previously, the plurality of differentstate machines include at least at least a first input state machine,which is dedicated to managing a state and a processing flow of a firsttype of input (e.g., speech input) which is received from a speechinterface for the one or more remotely connected personal assistants, aswell as at least a second input state machine dedicated to managing astate and a processing flow of a second type of input (e.g., other thanspeech input) which is received from one or more interfaces other thanthe speech interface of the one or more remotely connected personalassistants, and at least one dedicated output state machine, which isdedicated to managing a state and a processing flow of output providedto the one or more remotely connected personal assistants in response toat least the first and second type of input.

Next, the system receives first input from a client through a firstpersonal assistant (act 620). The first input comprises a first type ofinput (e.g., speech input) and the first input state machine controlsthe state and processing flow of the first input through the personalassistant server. Notably, this input is received asynchronously fromother input of other types (e.g., other than speech) that is received bythe system through the first personal assistant and/or from output thatis provided to the client through the personal assistant.

Next, the system transforms the first input (act 630), such as bytranslating or converting the first input into a different format, suchas speech to text, or another format change. This transformation mayalso include adding contextual information and/or appending the inputwith routing/addressing information for routing the input to aparticular back-end intelligence bot.

The system then sends the input, once transformed, to the appropriateand corresponding intelligence bot that the personal assistant is mappedto. In some instance, this may include sending the input to only asingle intelligence bot or, alternatively, to a plurality of differentintelligence bots. In some instances, the intelligence bot isproprietary to the personal assistant and they communicate withencrypted security keys known only to the personal assistant and thecorresponding intelligence bot(s). In other instances, the intelligencebot(s) comprise an existing personal assistant platform, such as aconventional Microsoft, Apple, Google, Amazon, Samsung or other personalassistant platform.

Next, the system obtains output to send to the first personal assistantwhich is associated with the first input (act 640). In some instances,this act includes performing additional transformations, similar tothose that were described above, for changing the format (but text tospeech this time), augmenting the output and/or appending with routinginformation.

The system also uses the different state machines to manage and updatethe states of the different input/output communication sessions, asdescribed before, with the different state machines.

Then, the system sends the first output to the first personal assistant(act 650), wherein the first input state machine controls the state andprocessing flow of the first output from the personal assistant serverto the first personal assistant without restricting the processing flowof the first output based on the state of the first input at the firstinput state machine. The transmission of the inputs and outputs may alsoinclude multiplexing and demultiplexing the input/output packetscontained in the communication streams transmitted between the differentsystems/components, as previously described.

In some instances, the system, prior to sending the output, receivessecond input from the personal assistant which is of a second input type(other than speech input). The system may obtain the second outputcorresponding to the second input prior to sending corresponding secondoutput (responsive to the second input) to the personal assistant, suchthat control and management of state and processing flow of the firstoutput and the second output are asynchronous from the control over thestate and processing flow of the first and second input state machines.

In some instances, the first input is only a portion of a set of firstinputs that are received by the personal assistant server as acontinuous string of speech received from the first personal assistant,and wherein the acts of transforming and obtaining are performed priorto receiving an entirety of the set of first inputs.

In some instances, the sending of the first output to the first personalassistant is further performed prior to the receiving the entirety ofthe set of first inputs.

In some instances, the transforming comprises converting the speech totext and providing a query to a remote back-end intelligence bot basedon the text. This query may include context information (such asreceived as part of the second input or prior input) that augments thetext.

In some instances, the query or transformed input provided to theintelligence bot(s) includes a security token received from the personalassistant and/or is encrypted with a key and which is usable by theback-end bot to validate the first personal assistant and/or to decryptinformation received in the query/input prior to the output beinggenerated by the intelligence bot(s) and prior to the output beingtransmitted to and/or received by the personal assistant server.

In some instances, the personal assistant is a proprietary personalassistant installed on a vehicle for interfacing with a user viahardware in the vehicle (e.g., microphone, speaker, touch/displayscreen) and wherein the back-end intelligence bot is maintainedseparately from the remote assistant server.

In some instances, the back-end intelligence bot comprises a separatepersonal assistant, including at least one of Siri™, Alexa™, Bixby™,Cortana™, or Google Personal Assistant.

As should be appreciated from the foregoing, the configuration andfunctionality of the disclosed personal assistant server and describedmethods can help facilitate asynchronous communications through thepersonal assistants that interface with the personal assistant serverand the deployment and use of proprietary personal assistants that canbe, in some instances, more secure and more nimble than conventionalpersonal assistants.

Embodiments of the present invention may comprise or utilize a specialpurpose or general-purpose computer including computer hardware, asdiscussed in greater detail below. Embodiments within the scope of thepresent invention also include physical and other computer-readablemedia for carrying or storing computer-executable instructions and/ordata structures. Such computer-readable media can be any available mediathat can be accessed by a general purpose or special purpose computersystem. Computer-readable media that store computer-executableinstructions are physical storage media. Computer-readable media thatcarry computer-executable instructions are transmission media. Thus, byway of example, and not limitation, embodiments of the invention cancomprise at least two distinctly different kinds of computer-readablemedia: physical computer-readable storage media and transmissioncomputer-readable media.

Physical computer-readable storage media includes RAM, ROM, EEPROM,CD-ROM or other optical disk storage (such as CDs, DVDs, etc.), magneticdisk storage or other magnetic storage devices, or any other mediumwhich can be used to store desired program code means in the form ofcomputer-executable instructions or data structures and which can beaccessed by a general purpose or special purpose computer.

A “network” is defined as one or more data links that enable thetransport of electronic data between computer systems and/or modulesand/or other electronic devices. When information is transferred orprovided over a network or another communications connection (eitherhardwired, wireless, or a combination of hardwired or wireless) to acomputer, the computer properly views the connection as a transmissionmedium. Transmissions media can include a network and/or data linkswhich can be used to carry or desired program code means in the form ofcomputer-executable instructions or data structures and which can beaccessed by a general purpose or special purpose computer. Combinationsof the above are also included within the scope of computer-readablemedia.

Further, upon reaching various computer system components, program codemeans in the form of computer-executable instructions or data structurescan be transferred automatically from transmission computer-readablemedia to physical computer-readable storage media (or vice versa). Forexample, computer-executable instructions or data structures receivedover a network or data link can be buffered in RAM within a networkinterface module (e.g., a “NIC”), and then eventually transferred tocomputer system RANI and/or to less volatile computer-readable physicalstorage media at a computer system. Thus, computer-readable physicalstorage media can be included in computer system components that also(or even primarily) utilize transmission media.

Computer-executable instructions comprise, for example, instructions anddata which cause a general-purpose computer, special purpose computer,or special purpose processing device to perform a certain function orgroup of functions. The computer-executable instructions may be, forexample, binaries, intermediate format instructions such as assemblylanguage, or even source code. Although the subject matter has beendescribed in language specific to structural features and/ormethodological acts, it is to be understood that the subject matterdefined in the appended claims is not necessarily limited to thedescribed features or acts described above. Rather, the describedfeatures and acts are disclosed as example forms of implementing theclaims.

Those skilled in the art will appreciate that the invention may bepracticed in network computing environments with many types of computersystem configurations, including, personal computers, desktop computers,laptop computers, message processors, hand-held devices, multi-processorsystems, microprocessor-based or programmable consumer electronics,network PCs, minicomputers, mainframe computers, mobile telephones,PDAs, pagers, routers, switches, and the like. The invention may also bepracticed in distributed system environments where local and remotecomputer systems, which are linked (either by hardwired data links,wireless data links, or by a combination of hardwired and wireless datalinks) through a network, both perform tasks. In a distributed systemenvironment, program modules may be located in both local and remotememory storage devices.

Alternatively, or in addition, the functionality described herein can beperformed, at least in part, by one or more hardware logic components.For example, and without limitation, illustrative types of hardwarelogic components that can be used include Field-programmable Gate Arrays(FPGAs), Program-specific Integrated Circuits (ASICs), Program-specificStandard Products (ASSPs), System-on-a-chip systems (SOCs), ComplexProgrammable Logic Devices (CPLDs), etc.

The present invention may be embodied in other specific forms withoutdeparting from its spirit or characteristics. The described embodimentsare to be considered in all respects only as illustrative and notrestrictive. The scope of the invention is, therefore, indicated by theappended claims rather than by the foregoing description. All changeswhich come within the meaning and range of equivalency of the claims areto be embraced within their scope.

What is claimed is:
 1. A personal assistant server for facilitating andsupporting customized personal assistant services and to at leastsupport asynchronous speech services for one or more remotely connectedpersonal assistants, the personal assistant server comprising: one ormore processors; a first input state machine which manages state andprocessing flow of a first type of input which is received from a speechinterface for the one or more remotely connected personal assistants,including at least a first personal assistant; a second input statemachine which manages state and processing flow of a second type ofinput which is received from one or more interfaces other than thespeech interface of the one or more remotely connected personalassistants; at least one output state machine which manages state andprocessing flow of output provided to the one or more remotely connectedpersonal assistants in response to at least the first and second type ofinput; one or more computer-readable media having stored thereoninstructions that are executable by the one or more processors to causethe personal assistant server to implement a method for supportingasynchronous speech services, the method comprising: providing aplurality of different state machines, the plurality of different statemachines including the first and second input state machines and the atleast one output state machine; receiving first input from the firstpersonal assistant comprising the first type of input, wherein the firstinput state machine controls the state and processing flow of the firstinput at the personal assistant server; transforming the first input;obtaining first output to send to the first personal assistant which isassociated with the first input; and sending the first output to thefirst personal assistant, wherein the first input state machine controlsthe state and processing flow of the first output from the personalassistant server to the first personal assistant asynchronously from theprocessing flow of the first input controlled by the first statemachine.
 2. The personal assistant server of claim 1, wherein prior tosending the output, the method further includes: receiving second inputfrom the personal assistant which is of the second input type; obtainingsecond output corresponding to the second input prior to sending thesecond output to the personal assistant; and controlling and managingstate and flow of the first output and the second output asynchronouslyfrom the control over the state and processing flow of the first andsecond input state machines.
 3. The personal assistant server of claim2, wherein the first input is only a portion of a set of first inputsthat are received by the personal assistant server as a continuousstring of speech received from the first personal assistant, and whereinthe acts of transforming and obtaining are performed prior to receivingan entirety of the set of first inputs.
 4. The personal assistant serverof claim 3, wherein the sending the first output to the first personalassistant is further performed prior to the receiving the entirety ofthe set of first inputs.
 5. The personal assistant server of claim 1,wherein the transforming comprises converting the speech to text andproviding a query to a remote back-end intelligence bot based on thetext.
 6. The personal assistant server of claim 5, wherein the queryincludes context information that augments the text.
 7. The personalassistant server of claim 6, wherein the context information is at leastpartially based on the second input.
 8. The personal assistant server ofclaim 7, wherein the obtaining includes obtaining the output which isprovided from the back-end intelligence bot.
 9. The personal assistantserver of claim 8, wherein the query includes a security token receivedfrom the personal assistant and which is usable by the back-end bot tovalidate the first personal assistant prior to the output being receivedby the personal assistant server.
 10. A method implemented by a personalassistant server for facilitating and supporting customized personalassistant services and to at least support asynchronous speech servicesfor one or more remotely connected personal assistants, the methodcomprising: providing a plurality of different state machines, theplurality of different state machines including: at least at least afirst input state machine, which is dedicated to managing a state and aprocessing flow of a first type of input which is received from a speechinterface for the one or more remotely connected personal assistants; atleast a second input state machine dedicated to managing a state and aprocessing flow of a second type of input which is received from one ormore interfaces other than the speech interface of the one or moreremotely connected personal assistants; and at least one dedicatedoutput state machine, which is dedicated to managing a state and aprocessing flow of output provided to the one or more remotely connectedpersonal assistants in response to at least the first and second type ofinput; receiving first input from a first personal assistant comprisingthe first type of input, wherein the first input state machine controlsthe state and processing flow of the first input at the personalassistant server; transforming the first input; obtaining first outputto send to the first personal assistant which is associated with thefirst input; and sending the first output to the first personalassistant, wherein the first input state machine controls the state andprocessing flow of the first output from the personal assistant serverto the first personal assistant without restricting the processing flowof the first output based on the state of the first input at the firstinput state machine.
 11. The method of claim 10, wherein prior tosending the output, the method further includes: receiving second inputfrom the personal assistant which is of the second input type; obtainingsecond output corresponding to the second input prior to sending thesecond output to the personal assistant; and controlling and managingstate and flow of the first output and the second output asynchronouslyfrom the control over the state and processing flow of the first andsecond input state machines.
 12. The method of claim 11, wherein thefirst input is only a portion of a set of first inputs that are receivedby the personal assistant server as a continuous string of speechreceived from the first personal assistant, and wherein the acts oftransforming and obtaining are performed prior to receiving an entiretyof the set of first inputs.
 13. The method of claim 12, wherein thesending the first output to the first personal assistant is furtherperformed prior to the receiving the entirety of the set of firstinputs.
 14. The method of claim 10, wherein the transforming comprisesconverting the speech to text and providing a query to a remote back-endintelligence bot based on the text.
 15. The method of claim 14, whereinthe query includes context information that augments the text.
 16. Themethod of claim 15, wherein the context information is at leastpartially based on the second input.
 17. The method of claim 16, whereinthe obtaining includes obtaining the output which is provided from theback-end intelligence bot.
 18. The method of claim 17, wherein the queryincludes a security token received from the personal assistant and whichis usable by the back-end bot to validate the first personal assistantprior to the output being received by the personal assistant server. 19.The method of claim 10, wherein the first personal assistant is aproprietary personal assistant installed on a vehicle for interfacingwith a user in the vehicle and wherein the back-end intelligence bot ismaintained separately from the remote assistant server.
 20. The methodof claim 10, wherein the back-end intelligence bot comprises a separatepersonal assistant, including at least one of Siri™, Alexa™, Bixby™Cortana™, or Google Personal Assistant.